L2 Regularization for Learning Kernels

  title={L2 Regularization for Learning Kernels},
  author={Corinna Cortes and Mehryar Mohri and Afshin Rostamizadeh},
The choice of the kernel is critical to the success of many learning algorithms but it is typically left to the user. Instead, the training data can be used to learn the kernel by selecting it out of a given family, such as that of non-negative linear combinations ofp base kernels, constrained by a trace or L1 regularization. This paper studies the problem of learning kernels with the same family of kernels but with anL2 regularization instead, and for regression problems. We analyze the… CONTINUE READING
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